Glucose rate increase detector: a meal detection module for the health monitoring system

ABSTRACT

A glucose rate increase detector (GRID) for use in an artificial pancreas (AP), wherein the GRID detects in a person persistent increases in glucose associated with a meal, and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the person to bolus for a meal, during open-loop control.

This invention was made with government support under Grant NumbersDP3DK094331 and ROIDK085628 awarded by the National Institutes of Health(NIH). The government has certain rights in the invention.

INTRODUCTION

The primary goal of the artificial pancreas (AP) is to eliminate theoccurrence of severe hypoglycemia and reduce the time spent inhyperglycemia (>180 mg/dL) in an effort to improve quality of life andreduce long-term complications.¹ Safe and effective control of type 1diabetes mellitus (T1DM) using an AP has been researched widely forseveral decades, with many advances, but several challenges remain,including overcoming large meal disturbances, the effects of exercise,and the delays associated with subcutaneous glucose sensing and insulindelivery.² One of the most challenging aspects of the diabetes therapyroutine is dealing with meals, and it has been shown that inaccurateestimation of meal sizes occurs frequently, resulting in additionalglucose fluctuations.³ Recent behavioral studies have also shown thatpeople with T1DM are interested in an automated system but are concernedwith relinquishing full control.^(4,5) Therefore, an automatic AP thatis safe and robust to daily living conditions and is trusted by theusers is critical.

The AP is a multi-layer device that will contain several features,including a core glucose controller, devices for monitoring of glucoseand possibly other biologically relevant compounds or signals, softwareto interface with the user, safety systems to monitor the status of thesystem, and telemedicine to convey information about the system to theuser and family and/or medical personnel. The core of the AP is thecontroller, the design of which has been explored by several researchteams, with promising results⁶⁻¹¹. Continuous glucose monitoring (CGM)devices and insulin pumps are continually being improved, and are at aperformance level that enables automatic control.^(12, 13) Currently,longer clinical trials with several meals and exercise are beingperformed with good results.^(6, 14) Generally, the trials with mealslarger than 50 g of carbohydrate (CHO) use a feed-forward approach,announcing meals and giving a full or partial bolus near mealtime.^(10, 15-17) This approach is taken due to the large glucoseexcursion caused by high CHO meals and the delays in subcutaneousglucose sensing and insulin action. For fully automatic control to bepossible with the currently available glucose sensing and insulindelivery routes, meal detection must be integrated into the controlscheme.

Several types of meal detection algorithms have been devised and studiedin recent years.¹⁸⁻²¹ In those cases, 1 minute sampling was used, whichmay increase the speed of detection and allow for increased accuracy. Atthis time, however, most CGMs provide data at a 5 minute sampling time.In Dassau et al.¹⁸, the algorithms were tuned using data with withheldboluses, enhancing the meal excursion and allowing for highersensitivity and faster detection. In addition, only isolated meals wereevaluated, not full traces with several meals, and other disturbances.Some of the algorithms were trained and tested on 1 minute simulationdata, with very little noise and disturbances.^(19, 20) This disclosureprovide, inter alia, an algorithm that has been trained and tested onclinical data that was in fully closed-loop mode, a reasonable model forthe actual conditions in which meal detection will be utilized.

The Glucose Rate Increase Detector (GRID) is a module of the HealthMonitoring System (HMS) that has been designed as a component of the APthat operates in parallel to the controller. The objective of the GRIDis to detect persistent increases in glucose associated with a meal, andtrigger a meal bolus to blunt the meal peak safely. It may be used inopen-loop control, closed-loop control with user input, or fullyautomatic closed-loop control.

SUMMARY OF THE INVENTION

Glucose management using continuous glucose monitoring and insulin pumpsas well as the use of an artificial pancreas (AP) system that implementsintensive insulin therapy has an inherent risk of adverse events such ashypoglycemia and hyperglycemia. Real-time prediction of pending adverseevents by the Health Monitoring System (HMS) would allow prevention byeither a corrective action or shifting to manual control. This inventionis based on continuous glucose monitoring (CGM) data that provides areliable layer of protection to insulin therapy, and provides a GlucoseRate Increase Detector (GRID) for the use with CGM Systems, Insulinpumps and the Artificial Pancreas (AP) for the detection of rises inglucose associated with meal events and for triggering of safe mealboluses.

The GRID is a module of the HMS that has been designed as a component ofthe AP that operates in parallel to the controller. The objective of theGRID is to detect persistent increases in glucose associated with ameal, and either trigger a meal bolus to blunt the meal peak safely(during closed-loop control) or alert the subject to bolus for a meal(open-loop control). It may be used in open-loop control, closed-loopcontrol with user input, or fully automatic closed-loop control.

The invention GRID provides a safety system that can accompany insulinpumps and continuous glucose monitoring systems, as well as artificialpancreas. The invention can be used to improve CGM capabilities indetecting meal disturbances and recommending correction boluses toprovide better glycemic control, including less time in hyperglycemia.

In one aspect the invention provides a GRID for use in an artificialpancreas (AP), wherein the GRID detects in a person persistent increasesin glucose associated with a meal, and either triggers a meal bolus toblunt meal peak safely, during closed-loop control, or alerts the personto bolus for a meal, during open-loop control.

In embodiments the GRID comprises a GRID algorithm which uses CGM datato estimate the rate of change (ROC) of glucose and detect meal-relatedglucose excursions, the algorithm comprising: a) a pre-processingsection to prepare the CGM data for analysis, b) an estimation sectionto approximate the ROC of glucose, and c) a detection section tologically pinpoint meal events.

In embodiments: a) in the pre-processing section, the algorithm filtersthe CGM data using a noise-spike filter; b) in the estimation section,the ROC of glucose is calculated using the first derivative of a 3-pointLagrangian interpolation polynomial, evaluated at the most recent point;and/or, c) the detection section comprises a logic wherein the detectionis positive and equal to 1 at the current point only if a correspondingfiltered point is above a value (G_(min)) chosen large enough to isolatepost-meal glucose values and to avoid the hypoglycemia region, andeither the last three ROC values are above G_(min) or the last two areabove G_(min), wherein the ROC cutoffs are chosen to isolate post-mealrises, and provides a hierarchical approach, with either two at a higherROC or three at a lower ROC, which allows faster detection with higherROC values.

In another aspect the invention provides a GRID configured to providethe steps of FIG. 1.

In another aspect the invention provides a HMS for real-time predictionof pending adverse events based on CGM data, comprising a subject GRIDand a controller, which provides prevention of the events by either acorrective action or shifting to manual control.

In another aspect the invention provides a method for providing areliable layer of protection to insulin therapy, comprising detectingrises in glucose associated with meal events and triggering safe mealboluses, wherein the detecting and triggering steps are performed with asubject GRID with a CGM system, an insulin pump or an artificialpancreas (AP).

In another aspect the invention provides an artificial pancreasprogrammed and configured to implement the protocol of FIG. 2.

The invention also provides corresponding algorithms for programmingcontrollers, HMS, and APs to effectively implement the disclosed steps.

The invention also provides a method comprising directing andoptionally, delivering, insulin delivery using a subject GRID,controller, HMS or AP.

The invention includes algorithms and insulin directing systemsessentially as described herein, and all combinations of the recitedparticular embodiments. All publications and patent applications citedin this specification are herein incorporated by reference as if eachindividual publication or patent application were specifically andindividually indicated to be incorporated by reference. Although theforegoing invention has been described in some detail by way ofillustration and example for purposes of clarity of understanding, itwill be readily apparent to those of ordinary skill in the art in lightof the teachings of this invention that certain changes andmodifications may be made thereto without departing from the spirit orscope of the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: Flow chart for GRID treatment protocols, followed after a mealis detected.

FIG. 2: Block diagram of a fully-automated AP with the GRID receivingCGM and insulin delivery information, and, upon detection of a meal,relaying a bolus recommendation to the Glucose Controller.

FIG. 3: Results for the GRID and Kalman Filter (KF), compared with thezone-MPC insulin response.

FIG. 4: Results of a cost-benefit analysis of sampling period on mealdetection metrics using in silico data.

FIG. 5: Time in range results of an 18 h study of adult subjects usingthe UVA/Padova simulator with CHO meal at 4.5 h.

FIG. 6: Time in range results of a 24 h in silico study of 10 adultsubjects using the UVA/Padova simulator with CHO meals.

DESCRIPTION OF PARTICULAR EMBODIMENTS OF THE INVENTION

Design of the Glucose Rate Increase Detector: Summary.

The Glucose Rate Increase Detector (GRID), a module of the HealthMonitoring System (HMS), has been designed to operate in parallel to theglucose controller to detect meal events and safely trigger a mealbolus.

The GRID algorithm was tuned on clinical data with 40-70 g CHO meals andtested on simulation data with 50-100 g CHO meals. Active closed andopen-loop protocols were executed in silico with various treatments,including automatic boluses based on a 75 g CHO meal and boluses basedon simulated user input of meal size. An optional function was used toreduce the recommended bolus using recent insulin and glucose history.

For closed-loop control of a three-meal scenario (50, 75 and 100 g CHO),the GRID improved median time in the 80-180 mg/dL range by 17% and inthe >180 range by 14% over unannounced meals, using an automatic bolusfor a 75 g CHO meal at detection. Under open-loop control of a 75 g CHOmeal, the GRID shifted the median glucose peak down by 73 mg/dL andearlier by 120 min and reduced the time >180 mg/dL by 57% over amissed-meal bolus scenario, using a full meal bolus at detection.

The GRID improved closed-loop control in the presence of large meals,without increasing late postprandial hypoglycemia. Users of basal-bolustherapy could also benefit from GRID as a safety alert for missed mealcorrections.

Methods

The modules of the HMS are each designed to monitor a specific componentof the AP, or type of adverse event or disturbance seamlessly withoutinterference. The most prevalent and risky occurrence is hypoglycemia.Thus, the Low Glucose Predictor (LGP) was designed to predict andprevent severe hypoglycemia in parallel to a controller, and has beenshown to be effective in clinic in combination with the zone-ModelPredictive Control (zone-MPC) controller.²²⁻²⁴

In an automatically controlled system, unmeasured disturbances such asmeals can cause large excursions out of the target zone, leading tohyperglycemia and, often, subsequent hypoglycemia due to over-deliveryin response to a meal. The GRID has been designed as the second modulein the HMS, for the express purpose of detecting meal excursions withhigh specificity and short reaction time.

HMS with GRID Design

The GRID algorithm uses CGM data to estimate the rate of change (ROC) ofglucose and detect meal-related glucose excursions. The GRID consists ofthree main subsections: 1) a pre-processing section to prepare the CGMdata for analysis, 2) an estimation section to approximate the ROC ofglucose, and 3) a detection section to logically pinpoint meal events.

In the pre-processing section, the algorithm filters the data using anoise-spike filter:²⁵

$\begin{matrix}{{G_{F,{NS}}(k)} = \left\{ {\begin{matrix}{G_{m}(k)} & {{{if}\mspace{14mu}{{{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}}}} \leq {\Delta\; G}} \\{{G_{F,{NS}}\left( {k - 1} \right)} - {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{F,{NS}}\left( {k - 1} \right)} - {G_{m}(k)}} \right)} > {\Delta\; G}} \\{{G_{F,{NS}}\left( {k - 1} \right)} + {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}} \right)} > {\Delta\; G}}\end{matrix},} \right.} & (0)\end{matrix}$

where k is the sampling instant, G_(F,NS) (k−1) is the previous filteredvalue from the noise spike filter, G_(F,NS) (k) is the filtered valueresulting from the noise-spike filter, G_(m) (k) is the measurement, andΔG is the maximum allowable ROC, set to 3 mg/dL in a one-minute period,to limit the ROC to a physiologically-probable value.^(26, 27) The dataare then passed through a low pass filter to damp high frequencyfluctuations:²⁵

$\begin{matrix}{{{G_{F}(k)} = {{\frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}{G_{F,{NS}}(k)}} + {\left( {1 - \frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}} \right){G_{F}\left( {k - 1} \right)}}}},} & (0)\end{matrix}$

where Δt is the sampling period, τ_(F) is the filter time constant, andG_(F) is the filtered value. The value for τ_(F) has been tuned tosmooth the data without introducing a long delay to optimize thespecificity and detection speed of the algorithm.

In the estimation section, the ROC of glucose is calculated using thefirst derivative of the 3-point Lagrangian interpolation polynomial,evaluated at the most recent point, as follows:^(18, 22)

$\begin{matrix}{{G_{F}^{\prime}(k)} \cong {{\frac{{t(k)} - {t\left( {k - 1} \right)}}{\left( {{t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}} \right)\left( {{t\left( {k - 2} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 2} \right)}} + {\frac{{t(k)} - {t\left( {k - 2} \right)}}{\left( {{t\left( {k - 1} \right)} - {t\left( {k - 2} \right)}} \right)\left( {{t\left( {k - 1} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 1} \right)}} + {\frac{{2{t(k)}} - {t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}}{\left( {{t(k)} - {t\left( {k - 1} \right)}} \right)\left( {{t(k)} - {t\left( {k - 2} \right)}} \right)}{{G_{F}(k)}.}}}} & (0)\end{matrix}$

In the detection logic, the detection, GRID⁺, is positive (equal to 1)at the current point only if the filtered point is above a value G_(min)and (^) either the last three ROC values are above G′_(min,3) or (∨) thelast two are above G′_(min,2):

$\begin{matrix}{{GRID}^{+} = \left\{ {\begin{matrix}1 & {{{if}\mspace{14mu}{G_{F}(k)}} > {G_{\min}\bigwedge\left( {\left( {{G_{F}^{\prime}\left( {{k - 2}:k} \right)} > G_{\min,3}^{\prime}} \right)\bigvee\left( {{G_{F}^{\prime}\left( {{k - 1}:k} \right)} > G_{\min,2}^{\prime}} \right)} \right)}} \\0 & {otherwise}\end{matrix}.} \right.} & (0)\end{matrix}$

The value of G_(min) is chosen large enough to isolate post-meal glucosevalues and to avoid the hypoglycemia region. The ROC cutoffs are chosento isolate post-meal rises and the hierarchical approach (with eithertwo at a higher ROC or three at a lower ROC) allows faster detectionwith higher ROC values.

Kalman Filter Algorithm

A standard Kalman Filter (KF) was used as a benchmark to evaluate theGRID algorithm. The KF was a version of the Optimal Estimation algorithmused by Palerm, et al.²⁸, modified for use with 5 min sampling. Thedetection logic was implemented as it was in the GRID, and tuned alongwith the number of states (two states including glucose value and rateof change of glucose and three states including the acceleration ofglucose as well) and the Q to R ratio for specificity and detectionspeed, resulting in slightly different tuning than the GRID.

Integration of HMS into Control Scheme

The knowledge of a meal event is helpful for disturbance rejection, andcan be used as a form of inferential control. Using GRID, the state ofthe system, with respect to meal events, is estimated. Once the discretemeal event is detected by the GRID module, a sequence of events toreject the disturbance is activated. There are two modes explored inthis paper, as shown in FIG. 1: The User-Input Mode, in which thedetection triggers an alert that requests meal information, which isthen used to deliver a full or partial meal bolus; and the AutomaticMode, in which a medium-sized meal bolus or a correction to low normalglucose levels is calculated and delivered automatically. Both modes canoperate with the Recent History Correction (RHC) function active toadjust the recommended bolus. The RHC has two functions: 1) to calculatethe insulin delivery over the last 60 min and subtract the amount overbasal from the recommended bolus, and 2) to calculate a correction to140 mg/dL for the lowest glucose value in the past 60 min and add it tothe recommended bolus. The correction to 140 mg/dL can be negative,reducing the recommended bolus if recent glucose values were on thelower end of the target zone. This action provides an additionalsafeguard against over-delivery. All of these calculations are based onthe clinical parameters of the subjects, including insulin tocarbohydrate ratios and correction factors.

The full incorporation of the HMS, including the GRID and the LGP isshown in FIGS. 1 and 2, with CGM information being sent to both LGP andGRID, and insulin information being sent to GRID to allow forcalculation of the RHC. The HMS operates in parallel with the controllerto minimize interference and also to reduce the likelihood of adversesafety events due to module failure.

Training and Validation

The GRID and KF algorithms were tuned using training data from clinicaltrials and tested on a validation set of clinical data and an in silicodata set, all with unannounced meals. As mentioned above, the algorithmswere tuned, in order of importance, for low detection time, low falsepositive rate (high specificity), and high number of meals positivelyidentified. Study details from all trials are shown in Table 1, withfurther results detailed in several references.²⁹⁻³²

Retrospective Clinical Data

The training data was comprised of 12 fully closed-loop, 24-h trialswith subjects with T1DM using zone-MPC with a target zone of 80-140mg/dL and HMS with LGP, performed at the Sansum Diabetes ResearchInstitute using the Artificial Pancreas System (APS©).³³ The subjectswere given small to medium-sized meals (40-50 g CHO) and performed 30min of moderate exercise, with some subjects receiving 16 g CHO snacksbefore exercise, and several receiving 16 g rescue CHO per the HMS. Allsubjects used Dexcom® SEVEN® PLUS, (Dexcom® San Diego, Calif.) CGMs witha 5 min sampling period, and received subcutaneous insulin delivery.

After tuning the algorithms, validation was performed on data from aseparate set of clinical trials with different subjects, all withT1DM.³⁴ Again, zone-MPC with HMS was used in the AP system. Subjectsconsumed meals of 40-70 g CHO and several received 16 g rescue CHO perthe HMS.

In Silico Trial Testing

To further compare sets of tuning parameters, in silico trials wereconducted using the Food and Drug Administration (FDA)-acceptedUVA/Padova metabolic simulator consisting of 10 adult subjects. Thesimulation was started at 3:00 am and closed-loop control using zone-MPCwith Insulin-on-board (IOB) input constraints was initiated at 5:00 am.The zone-MPC target glucose zones were 80-140 mg/dL from 7:00 am to10:00 pm and 110-170 mg/dL from midnight to 5:00 am, with smoothtransitions in between.²⁴ Meals of 50, 75, and 100 g were given at 7:00am, 1:00 pm, and 6:00 pm, respectively, with control continuing until3:00 am the next day. Data were collected using a sampling time of 1 minand tested using the GRID and KF algorithms after down-sampling to 5min.

Cost-Benefit Analysis

The success of automatically rejecting the meal disturbance is highlydependent on the speed of detection. If detected too late, it may be ofno use, or even cause hypoglycemia if too much insulin is delivered inexcess of the controller correction. The simulator provides a samplingperiod of 1 min, so an analysis of the benefit of faster sampling rateon speed of detection, rise at detection, and the percentage of mealsdetected was performed.

Prospective Application

Several in silico scenarios with GRID actively running and triggeringmeal boluses were performed to test the algorithm. All scenarios used asampling period of 5 min

Standard Care Alert

For subjects on standard basal-bolus therapy, meal boluses are sometimesmissed, especially by adolescents or busy adults.³⁵ A missed meal bolusduring standard basal-bolus therapy was simulated, to evaluate theability of the algorithm to inform a CGM user of the missed bolus in atimely manner, blunting the glucose peak and decreasing the time inhyperglycemia. An 18 h scenario with a 50, 75, or 100 g CHO meal at 4.5h was simulated with several protocols, shown in Table 2. User-inputboluses are delivered at the cycle after detection to simulate the delayof waiting for user response.

Zone-MPC with Inferential Control

As shown above, the GRID was integrated into the control scheme as aform of inferential control, by detecting the meal disturbance,calculating an insulin bolus to reject the disturbance, and feeding thisinformation to the zone-MPC controller. The LGP module of the HMS wasalso active, with a prediction threshold of 65 mg/dL and an activationthreshold of 100 mg/dL.^(22, 23, 36, 37) A 24 h scenario with threemeals of 50, 75, and 100 g CHO was performed, as above in the CHO perthe HMS.

In Silico Trial Testing section. Control protocols are shown in

Table 3.

Results and Discussion: Training and Validation

Based on the training data, the best set of tuning parameters for theGRID was the following: τ=6 min, G_(min)=130 mg/dL, G′_(min,2)=1.5mg/dL/min, and G′_(min,2)=1.6 mg/dL/min. This combination of parametersresulted in a mean time to detection of 42 min from the start of themeal, 87.5% of meals detected within 2 h, and 1.6 false positivedetections per day. Due to the large number of snacks and hypoglycemiarescues, adjusted values for meals detected and false positive alarmswere calculated, resulting in 65% of all carbohydrate ingestions beingdetected and only 0.58 false positive detections per day. For KF, thebest set of tuning parameters was a two-state estimate with Q:R=0.1,G_(min)=140 mg/dL, G′_(min,3)=1.75 mg/dL/min, and G′_(min,2)=1.85mg/dL/min. The mean time to detection was 45 min from the start of themeal, 79.2% of meals were detected within 2 h, and 1.5 false positivedetections occurred per day. The adjusted calculation resulted in 57% ofall carbohydrate ingestions being detected and only 0.58 false positivedetections per day. Both algorithms were compared to the insulinresponse by the controller, quantified as the time from the start of themeal to the time when the average delivery over 15 min was more than 50%above the basal rate. The insulin response was compared because,depending on the glucose values and trend at meal time, and thesubject's sensitivity to CHO and insulin, some meals did not result in apronounced excursion. In these cases, a positive meal detection alert isnot expected or necessary. In both validation and simulation, bothalgorithms performed with higher detection rates and lower falsepositive rates than in the training set. In simulation, detection wasfaster for the GRID. Results of GRID and KF on the training, validation,and simulation data are shown in FIG. 3, with paired t-test resultscomparing GRID to KF shown above the boxes with asterisks or circledasterisks when statistically significant.

Cost-Benefit Analysis

The cost of faster sampling can be seen in the form of expensive sensorsand increased energy consumption by the sensors, receivers, andcontrollers, which could lead to shorter life and increased monetarycost. As the glucose sampling period increases, it is expected thatdetection of meals will deteriorate, so faster sampling period couldimprove the performance of a controller with inferential control usingmeal detection. The cost-benefit analysis of this system was performedby testing sampling times of 1 to 30 min, as seen in FIG. 4. For mealsabove 50 g CHO, a 5 min increase in time to detection and a 15 mg/dLincrease in glucose at detection resulted when increasing from 1 to 5min sampling, while all meals were still detected. Metrics for smallermeals were more impacted, due to a less pronounced glucose excursion.Small meals can generally be dealt with without the use of additionalinsulin from meal detection. This result indicates that a samplingperiod of 5 min is sufficient for meal detection of medium to largemeals but, if reliable 1 min sampling was readily and cheaply available,meal detection could be improved.

Prospective Application; Standard Care Alert

The GRID yielded positive meal detections approximately 40-45 min fromthe start of meals, and reduced both the meal peaks and the duration ofhyperglycemia, when compared to unannounced meals. The result of thedelay in the bolus during GRID-active protocols is a large improvementover the missed meal protocol (B).

The time in range results of single meals of 50, 75, or 100 g CHO withopen-loop therapy are shown in FIG. 5, with paired t-test resultscomparing the unannounced protocol (B) to the others shown above theboxes with asterisks or circled asterisks when statisticallysignificant. In the case of open-loop control, a full bolus with RHC isrecommended at detection (E), with significantly better time in rangeand much less time in the hyperglycemia range than the unannouncedprotocol (B).

Zone-MPC with Inferential Control

Detailed results of the zone-MPC protocols were determined, with time inrange in FIG. 6. The GRID yielded positive meal detections approximately40-45 min from the start of the meal, and delivered a calculated bolus,as described above. For the Automatic Mode bolus protocol (E), the mealpeak and time in the 80-180 range were significantly better than in theunannounced case (B). For all meals, the time in the 80-180 range wasimproved over the unannounced protocol (B) by both the Automatic Modebolus protocol (E), and User-Input Mode protocol (D). Although up tofive hypoglycemia treatments were given per HMS with LGP, seven out often subjects had no hypoglycemia (<70 mg/dL), and the number oftreatments and time under 70 mg/dL was not significantly higher for anyof the protocols when compared to announced meals. In the case ofclosed-loop control, a full bolus for a 75 g CHO meal with RHC isrecommended at detection (E), with significantly better time in rangeand much less time in the hyperglycemia range than the unannouncedprotocol (B). Detailed results are shown in Table 4.

CONCLUSIONS

The GRID module of the HMS was designed to accurately and quicklyidentify meal glucose excursions and logically recommend an insulinbolus to reject the meal disturbance. The algorithm was tuned usingnoisy clinical trial data with unannounced meals and several snacks, andthe same controller used in the simulations. It should be noted that,while tuning for speed of detection was the first priority, anyalgorithms that produced more than 2.0 false positive detections per daywere excluded. Even with those algorithms included, the fastestdetection time would have been 35 min for KF or GRID. Thus, withcontrolled data and medium-sized meals, a 30+ min delay for mealdetection based on CGM data is the limit of detection speed.

The GRID is designed as a parallel module to the controller that focuseson meal detection, to trigger a rejection of the meal disturbance. Thisapproach provides a more bolus-like meal response by the controller, andthe IOB constraint keeps over-delivery from occurring, essentiallyfront-loading the insulin for the meal response without need for outsideinput. With the knowledge that the meal detection is delayed by at least30 min, the disturbance rejection action was logically modified with bythe RHC function, which reduced the recommended bolus by recent deliveryand adjusted for recent glucose history.

During closed-loop control, the GRID was able to improve control in thepresence of large meals, without increasing the instances ofhypoglycemia or increasing the time in the hypoglycemia range (<70mg/dL), as seen in FIG. 6 and Table 4. In addition, fast recognition ofmissed meal boluses in open-loop mode, for users on standard therapy cangreatly improve the time in range and serve as a safety alert for usersof the currently available devices.

LEGENDS TO THE FIGURES

FIG. 1: Flow chart for GRID treatment protocols, followed after a mealis detected. Automatic Mode protocols are in the box surrounded by adashed line and User-Input Mode protocols are in the box surrounded bythe dotted line.

FIG. 2: Block diagram of a fully-automated AP with the GRID receivingCGM and insulin delivery information, and, upon detection of a meal,relaying a bolus recommendation to the Glucose Controller. The HMS isoutlined in a black solid line, with sub-modules GRID and LGP outlinedin double lines, the controller in black solid and physical devices andthe subject in dotted lines.

FIG. 3: Results for the GRID (no fill) and KF (45 degree lines),compared with the zone-MPC insulin response (45 degree cross hatches).(A) Training set from a 12-subject clinical trial using zone-MPC withtwo unannounced meals (50 and 40 g CHO); (B) Validation set from a10-subject clinical trial using zone-MPC, with three unannounced meals(70, 40, and 70 g CHO); and (C) Simulation set from a 10-subjectscenario, with three unannounced meals (50, 75, and 100 g CHO). (1) Timeof detection; (2) rise in glucose at detection; (3) the percentage ofmeals that were detected within 2 h; (4) rate of false positivedetections. The metrics with statistically significantly differentresults from the GRID algorithm (paired t-test, p<0.05 and p<0.01) areshown above the boxes with asterisks and circled asterisks,respectively. Means are shown as crosses and totals in x's.

FIG. 4: Results of a cost-benefit analysis of sampling period on mealdetection metrics using in silico data. Meals of 25, 50, 75, or 100 gCHO with no bolus are shown in diamonds, squares, circles, andtriangles, respectively. Both Zone-MPC, shown in dotted lines with opensymbols, or Standard Care (basal/bolus), shown with solid lines andfilled symbols, control types were tested. The GRID was executed on thedata with sampling periods varying from 1 to 30 min (A) Mean rise inglucose from meal commencement to time of detection; (B) mean time frommeal commencement to time of detection; and (C) percent of mealsdetected within 2 h from the start of the meal.

FIG. 5: Time in range results of an 18 h in silico study of 10 adultsubjects using the UVA/Padova simulator with, from top to bottom, 50 g(1), 75 g (2), or 100 g (3) CHO meal at 4.5 h. Scenarios (A-F)correspond to (A-F) in FIG. 5 and Table 2 in no fill, black fill, 45degree cross hatches, 45 degree lines (from bottom left to top right),−45 degree lines (from top left to bottom right), and horizontal lines,respectively. Means are shown in black crosses, and medians in dots withwhite borders. Protocols that have statistically significantly differentresults from the unannounced (B) protocol (paired t-test, p<0.05 andp<0.01) are shown above the boxes with asterisks, *, and circledasterisks, {circle around (*)}, respectively.

FIG. 6: Time in range results of a 24 h in silico study of 10 adultsubjects using the UVA/Padova simulator with 50, 75, and 100 g CHO mealsat 7:00, 13:00, and 19:00, respectively. Scenarios (A-G) correspond to(A-G) in

Table 3 in no fill, black fill, 45 degree cross hatches, 45 degree lines(from bottom left to top right), −45 degree lines (from top left tobottom right), horizontal lines, and vertical lines, respectively. Meansare shown in black crosses, and medians in black dots with whiteborders. Protocols that have statistically significantly differentresults from the unannounced (B) protocol (paired t-test, p<0.05 andp<0.01) are shown above the boxes with asterisks, *, and circledasterisks, {circle around (*)}, respectively.

REFERENCES

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TABLE 1 Characteristics of training clinical datasets, validationclinical datasets, and simulation testing set. Zone-MPC with unannouncedmeals was used during each trial and simulation. Values after the numberof males are presented as median (range) except where indicated. Allranges are calculated with CGM data. A) Training B) Validation C)Simulation N, datasets 12 10 10 Male sex, number  4  7 — Age, y   53(28-62)   52 (30-62) — Height, cm  167 (157-193)  170 (156-178) —Weight, kg   70 (53-132)   65 (54-94)   72 (46-99) Total Daily Basal, U18.4 (11.6-46.2)   24 (7.5-39.5) 29.7 (22-45.7) Total Daily Insulin, U  33 (22.9-73.2)   38 (23.1-105)   43 (34-72) Default CarbohydrateRatio, g CHO/U 10.5 (6.33-15) 11.5 (3.5-20) 16.5 (9-22) HypoglycemiaTreatments^(a), g CHO   56 (16-112)   24 (0-112)   0 (0-80) DefaultCorrection Factor, mg/dL/U 51.5 (25-100)   58 (12.5-70) 42.5 (26-53)Overall duration, h   22 (19-24)   24 (22-25) 24 Time <50 mg/dL, %   0(0-1.6)   0 (0-14)   0 (0-1.3) Time <70 mg/dL, %   2 (0-6.4)  1.7 (0-20)  0 (0-5.9) Time 70-80 mg/dL, %  2.5 (0.76-6.8)  1.7 (0-13)   0 (0-3.9)Time 80-140 mg/dL, %   46 (15-65)   26 (15-41)   44 (29-53) Time 140-180mg/dL, %   22 (4.5-39)   19 (6.8-25)   18 (9.3-26) Time 180-250 mg/dL, %  18 (4.2-41)   24 (7.1-45)   26 (14-40) Time >250 mg/dL, %  7.6 (0-20)  25 (4.6-53)  9.5 (0-36) Total Insulin Delivered, U 22.3 (14.6-53.8)37.2 (14.7-56.2) 35.8 (29.3-50.8) Size of Meal 1, g CHO   50 (50-51)  70 (70-70)   50 (50-50) Baseline Glucose at Meal 1, mg/dL  112(63-204)  108 (58-244)  117 (98-139) Time of Meal 1^(a) 19:25 ± 00:3018:54 ± 00:08  7:00 Peak Glucose after Meal 1, mg/dL  218 (128-266)  286(217-366)  229 (178-286) Time of Peak Glucose after Meal 1, min^(b)  100(60-115)  113 (70-120)  113 (77-120) Size of Meal 2, g CHO   40 (38-40)  40 (40-40)   75 (75-75) Baseline Glucose at Meal 2, mg/dL  111(79-160)  126 (67-185)  116 (91-138) Time of Meal 2^(a) 06:58 ± 00:0807:52 ± 00:07 13:00 Peak Glucose after Meal 2, mg/dL  285 (176-378)  269(164-387)  250 (219-423) Time of Peak Glucose after Meal 2, min^(b)   91(65-115)   90 (75-115)  107 (73-120) Size of Meal 3, g CHO —   70(70-70)  100 (100-100) Baseline Glucose at Meal 3, mg/dL —  150 (39-226)  97 (70-141) Time of Meal 3^(a) — 12:52 ± 00:07 19:00 Peak Glucoseafter Meal 3, mg/dL —  291 (83-401)  310 (233-509) Time of Peak Glucoseafter Meal 3, min^(b) —  115 (60-120)  111 (86-120) ^(a)mean ± standarddeviation, ^(b)Calculated as peak within 2 h of the start of the meals.

TABLE 2 Standard care alert simulation protocols. Announced GRID GRIDRecent History Bolus Size Protocol Meal Mode Protocol Correction Active(%) A Yes Off — — 100 B No Off — — 0 C No User-Input Partial No 50 D NoUser-Input Partial Yes 50 E No User-Input Full No 100 F No User-InputFull Yes 100

TABLE 3 Zone-MPC with inferential control simulation protocols. RecentHistory Bolus Announced GRID GRID Correction Size Protocol Meal ModeProtocol Active (%) A Yes Off — — 100 B No Off — — 0 C No User-InputPartial Yes 50 D No User-Input Full Yes 100 E No Automatic 75 g CHO MealYes 100 Bolus F No Automatic Correction to 80 Yes 100 mg/dL G NoAutomatic Minimum of E Yes 100 and FTable 4: Characteristics of an in silico study of 10 adult subjectsusing the UVa/Padova simulator. Scenarios are A-G as described in

TABLE 3 A B C D Time <50 mg/dL, % 0 (0-0) 0 (0-1.3) 0 (0-0) 0 (0-4.5)Time 50-70 mg/dL, % 0 (0-0) 0 (0-4.6) 0 (0-3.8) 0 (0-3.5) Time 70-80mg/dL, % 0 (0-2.0) 0 (0-3.9) 0 (0-3.2) 0 (0-4.9) Time 80-180 mg/dL, % 89(72-96) 

57 (44-78) 63 (53-81) 73 (48-85) 

Time >180 mg/dL, % 9.9 (3.6-28) 

39 (22-51) 34 (19-42) 25 (15-39) 

Time >250 mg/dL, % 0 (0-0) 

9.5 (0-36) 7.8 (0-30) 3.6 (0-17) Total Insulin Delivered, U 40 (31-64)36 (29-51) 37 (30-54) 38 (30-60) Hypoglycemia Treatments, g CHO 0 (0-16)0 (0-80) 0 (0-32) 0 (0-64) Size of Meal 1, g CHO 50 50 50 50 BaselineGlucose at Meal 1, mg/dL 117 (98-139) 117 (98-139) 117 (98-139) 117(98-139) Time of Meal 1  7:00  7:00  7:00  7:00 Peak Glucose after Meal1, mg/dL 183 (148-197) 

229 (178-286) 224 (178-283) 221 (178-259) Time of Peak Glucose fromStart of Meal 1, min 81.5 (53-116) 113 (77-120) 108 (77-120) 104(76-119) Time 80-180 mg/dL from Start of Meal 1 to Meal 2% 93 (75-100) 

58 (36-85) 62 (44-85) 73 (52-85) * Glucose at Detection for Meal 1,mg/dL 158 (147-173) 159 (147-169) 159 (147-169) 159 (147-169) Time ofDetection from Start of Meal 1, min 48 (45-55) 43 (40-45) 43 (40-45) 43(40-45) Equivalent Meal Size for Bolus, g CHO 50 — 11 (6.6-17) 36(32-42) Size of Meal 2, g CHO 75 75 75 75 Baseline Glucose at Meal 2,mg/dL 108 (95-123) 116 (91-138) 114 (91-139) 105 (87-125) Time of Meal 213:00 13:00 13:00 13:00 Peak Glucose after Meal 2, mg/dL 189 (161-222) 

250 (219-423) 247 (216-421) 235 (204-344) Time of Peak Glucose fromStart of Meal 2, min 78.5 (58-120) 107 (73-120) 107 (72-119) 99.5(69-115) Time 80-180 mg/dL from Start of Meal 2 to Meal 3% 89 (67-100) 

42 (33-64) 53 (39-71) 64 (39-82) 

Glucose at Detection for Meal 2, mg/dL 149 (144-156) 156 (152-198) 153(148-199) 159 (147-188) Time of Detection from Start of Meal 2, min 40(30-50) 40 (30-55) 40 (30-55) 43 (30-55) Equivalent Meal Size for Bolus,g CHO 75 — 21 (3.3-31) 57 (48-66) Size of Meal 3, g CHO 100  100  100 100  Baseline Glucose at Meal 3, mg/dL 96.5 (86-137) 97 (70-141) 92.5(68-132) 92 (84-199) Time of Meal 3 19:00 19:00 19:00 19:00 Peak Glucoseafter Meal 3, mg/dL 215 (186-241) 

310 (233-509) 294 (223-179) 276 (223-397) Time of Peak Glucose fromStart of Meal 3, min 75 (51-98) 111 (86-120) 101 (78-118) 85.5 (64-102)Time 80-180 mg/dL from Start of Meal 3 to end, % 83 (60-96) 

52 (19-71) 63 (44-75) 71 (25-82) * Glucose at Detection for Meal 3,mg/dL 153 (144-163) 172 (145-244) 158 (150-209) 165 (146-319) Time ofDetection from Start of Meal 3, min 40 (25-50) 40 (25-90) 37 (25-45) 40(25-50) Equivalent Meal Size for Bolus, g CHO 100  — 31 (18-42) 80(64-89) E F G Time <50 mg/dL, % 0 (0-1.5) 0 (0-2.6) 0 (0-2.8) Time 50-70mg/dL, % 0 (0-4.7) 0 (0-5.6) 0 (0-5.7) Time 70-80 mg/dL, % 0 (0-4.9) 0(0-3.9) 0 (0-4.0) Time 80-180 mg/dL, % 74 (54-85) 

66 (46-82) 66 (51-82) Time >180 mg/dL, % 25 (15-35) 

31 (18-42) * 31 (18-40) * Time >250 mg/dL, % 4.7 (0-13) 6.7 (0-20) 6.7(0-18) Total Insulin Delivered, U 38 (31-61) 37 (30-54) 37 (30-54)Hypoglycemia Treatments, g CHO 0 (0-64) 0 (0-80) 0 (0-64) Size of Meal1, g CHO 50 50 50 Baseline Glucose at Meal 1, mg/dL 117 (98-139) 117(98-139) 117 (98-139) Time of Meal 1  7:00  7:00  7:00 Peak Glucoseafter Meal 1, mg/dL 212 (178-238) * 222 (178-258) 222 (178-258) Time ofPeak Glucose from Start of Meal 1, min 89.5 (75-119) 106 (77-120) 106(77-120) Time 80-180 mg/dL from Start of Meal 1 to Meal 2% 80 (56-90) 

68 (51-85) 68 (51-85) Glucose at Detection for Meal 1, mg/dL 159(147-169) 159 (147-169) 159 (147-169) Time of Detection from Start ofMeal 1, min 13 (40-45) 43 (40-45) 43 (40-45) Equivalent Meal Size forBolus, g CHO 63 (60-69) 22 (15-29) 22 (15-29) Size of Meal 2, g CHO 7575 75 Baseline Glucose at Meal 2, mg/dL 94 (78-123) 109 (91-124) 109(91-124) Time of Meal 2 13:00 13:00 13:00 Peak Glucose after Meal 2,mg/dL 232 (197-330) 242 (212-361) 242 (212-361) Time of Peak Glucosefrom Start of Meal 2, min 97 (67-118) 104 (71-120) 104 (71-120) Time80-180 mg/dL from Start of Meal 2 to Meal 3% 63 (40-83) 

52 (37-74) 52 (37-74) Glucose at Detection for Meal 2, mg/dL 157(149-185) 156 (147-167) 156 (147-167) Time of Detection from Start ofMeal 2, min 45 (30-55) 43 (30-55) 43 (30-55) Equivalent Meal Size forBolus, g CHO 58 (56-62) 22 (15-31) 22 (15-31) Size of Meal 3, g CHO 100 100  100  Baseline Glucose at Meal 3, mg/dL 93 (83-190) 99 (84-188) 99(84-188) Time of Meal 3 19:00 19:00 19:00 Peak Glucose after Meal 3,mg/dL 279 (222-324) 299 (225-383) 299 (225-383) Time of Peak Glucosefrom Start of Meal 3, min 87.5 (65-115) 103 (83-120) 103 (83-120) Time80-180 mg/dL from Start of Meal 3 to end, % 66 (39-79) * 59 (23-76) 59(36-76) Glucose at Detection for Meal 3, mg/dL 165 (147-273) 159(154-351) 159 (154-351) Time of Detection from Start of Meal 3, min 40(25-50) 37 (25-60) 37 (25-60) Equivalent Meal Size for Bolus, g CHO 58(45-79) 24 (15-57) 24 (15-56) Values are presented as median (range).Metrics that are statistically significantly different results from theunannounced (B) protocol (paired t-test, p < 0.05 and p < 0.01) areshown after the values with asterisks, *, and circled asterisks, 

 , respectively.

What is claimed is:
 1. A glucose rate increase detector (GRID) operative in conjunction with a continuous glucose monitoring (CGM) system, a controller and an insulin pump, wherein the GRID detects in a user persistent increases in glucose associated with a meal, and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the user to bolus for a meal, during open-loop control, wherein: the GRID is configured to operate in two modes: (a) a user-input mode, in which the user enters meal information, which the GRID uses to calculate the meal bolus, and (b) an automatic mode, in which the GRID automatically calculates the meal bolus or a glucose level correction, wherein the GRID comprises an algorithm which uses CGM data to estimate the rate of change (ROC) of glucose and detect meal-related glucose excursions, the algorithm comprising: i) a pre-processing section which uses a noise-spike filter to filter the CGM data for glucose ROC estimation, ii) an estimation section which uses the filtered CGM data to calculate glucose ROCs, and iii) a detection section which uses the calculated glucose ROCs to logically pinpoint meal events; the GRID configured to detect in the user persistent increases in blood glucose concentration associated with meal events, wherein the GRID triggers the controller to actuate the pump to deliver safe meal boluses to blunt meal peak safely, or to shift to manual control, during closed-loop control, or alerting the user to bolus for a meal, during open-loop control, wherein: (a) the pre-processing section filters the data using a noise-spike filter: ${G_{F,{NS}}(k)} = \left\{ \begin{matrix} {G_{m}(k)} & {{{if}\mspace{14mu}{{{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}}}} \leq {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} - {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{F,{NS}}\left( {k - 1} \right)} - {G_{m}(k)}} \right)} > {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} + {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}} \right)} > {\Delta\; G}} \end{matrix} \right.$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS) (k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement, and ΔG is the maximum allowable ROC, set to 3 mg/dL in a one-minute period, to limit the ROC to a physiologically-probable value, and the data are then passed through a low pass filter to damp high frequency fluctuations: ${{G_{F}(k)} = {{\frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}{G_{F,{NS}}(k)}} + {\left( {1 - \frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}} \right){G_{F}\left( {k - 1} \right)}}}},$ where Δt is the sampling period, τ_(F) is the filter time constant, and G_(F) is the filtered value, wherein the value for τ_(F) has been tuned to smooth the data without introducing a long delay to optimize the specificity and detection speed of the algorithm; or (b) in the estimation section, the ROC of glucose is calculated using the first derivative of a 3-point Lagrangian interpolation polynomial, evaluated at the most recent point, as, as follows: ${G_{F}^{\prime}(k)} \cong {{\frac{{t(k)} - {t\left( {k - 1} \right)}}{\left( {{t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}} \right)\left( {{t\left( {k - 2} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 2} \right)}} + {\frac{{t(k)} - {t\left( {k - 2} \right)}}{\left( {{t\left( {k - 1} \right)} - {t\left( {k - 2} \right)}} \right)\left( {{t\left( {k - 1} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 1} \right)}} + {\frac{{2{t(k)}} - {t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}}{\left( {{t(k)} - {t\left( {k - 1} \right)}} \right)\left( {{t(k)} - {t\left( {k - 2} \right)}} \right)}{G_{F}(k)}}}$ where k is the sampling instant, G_(F,NS) (k−1) is the previous filtered value from the noise spike filter, G_(F,NS)(k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement; or (c) the detection section comprises a logic wherein the detection is positive and equal to 1 at the current point only if a corresponding filtered point is above a value (G_(min)) and (^) either the last three ROC values are above G′_(min,3) or (∨) the last two are above G′_(min,2): ${GRID}^{+} = \left\{ \begin{matrix} 1 & \begin{matrix} {{{if}\mspace{14mu}{G_{F}(k)}} > {G_{\min}\bigwedge\left( {\left( {{G_{F}^{\prime}\left( {k - {2\text{:}k}} \right)} > G_{\min,3}^{\prime}} \right)\bigvee} \right.}} \\ \left. \left( {{G_{F}^{\prime}\left( {k - {1\text{:}k}} \right)} > G_{\min,2}^{\prime}} \right) \right) \end{matrix} \\ 0 & {otherwise} \end{matrix} \right.$ wherein k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, the value of G_(min) is chosen large enough to isolate post-meal glucose values and to avoid the hypoglycemia region, and the ROC cutoffs are chosen to isolate post-meal rises and the hierarchical approach, with either two at a higher ROC or three at a lower ROC, allows faster detection with higher ROC values.
 2. The detector of claim 1 wherein the pre-processing section filters the data using a noise-spike filter: ${G_{F,{NS}}(k)} = \left\{ \begin{matrix} {G_{m}(k)} & {{{if}\mspace{14mu}{{{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}}}} \leq {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} - {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{F,{NS}}\left( {k - 1} \right)} - {G_{m}(k)}} \right)} > {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} + {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}} \right)} > {\Delta\; G}} \end{matrix} \right.$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS)(k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement, and ΔG is the maximum allowable ROC, set to 3 mg/dL in a one-minute period, to limit the ROC to a physiologically-probable value, and the data are then passed through a low pass filter to damp high frequency fluctuations: ${{G_{F}(k)} = {{\frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}{G_{F,{NS}}(k)}} + {\left( {1 - \frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}} \right){G_{F}\left( {k - 1} \right)}}}},$ where Δt is the sampling period, τ_(F) is the filter time constant, and G_(F) is the filtered value, wherein the value for τ_(F) has been tuned to smooth the data without introducing a long delay to optimize the specificity and detection speed of the algorithm.
 3. The detector of claim 1, wherein in the estimation section, the ROC of glucose is calculated using the first derivative of a 3-point Lagrangian interpolation polynomial, evaluated at the most recent point, as, as follows: ${G_{F}^{\prime}(k)} \cong {{\frac{{t(k)} - {t\left( {k - 1} \right)}}{\left( {{t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}} \right)\left( {{t\left( {k - 2} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 2} \right)}} + {\frac{{t(k)} - {t\left( {k - 2} \right)}}{\left( {{t\left( {k - 1} \right)} - {t\left( {k - 2} \right)}} \right)\left( {{t\left( {k - 1} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 1} \right)}} + {\frac{{2\;{t(k)}} - {t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}}{\left( {{t(k)} - {t\left( {k - 1} \right)}} \right)\left( {{t(k)} - {t\left( {k - 2} \right)}} \right)}{G_{F}(k)}}}$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS)(k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement.
 4. The detector of claim 1, wherein the detection section comprises a logic wherein the detection is positive and equal to 1 at the current point only if a corresponding filtered point is above a value (G_(min)) and (^) either the last three ROC values are above G′_(min,3) or (∨) the last two are above G′_(min,2): ${GRID}^{+} = \left\{ \begin{matrix} 1 & {{{if}\mspace{14mu}{G_{F}(k)}} > {G_{m\; i\; n}\bigwedge\left( {\left( {{G_{F}^{\prime}\left( {{k - 2}:k} \right)} > G_{{m\; i\; n},3}^{\prime}} \right)\bigvee\left( {{G_{F}^{\prime}\left( {{k - 1}:k} \right)} > G_{{m\; i\; n},2}^{\prime}} \right)} \right)}} \\ 0 & {otherwise} \end{matrix} \right.$ wherein k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, the value of G_(min) is chosen large enough to isolate post-meal glucose values and to avoid the hypoglycemia region, and the ROC cutoffs are chosen to isolate post-meal rises and the hierarchical approach, with either two at a higher ROC or three at a lower ROC, allows faster detection with higher ROC values.
 5. A health monitoring system (HMS) for real-time prediction of pending adverse events based on continuous glucose monitoring (CGM) data, comprising the glucose rate increase detector (GRID) of claim 1 and a controller, which provides prevention of the events by either a corrective action or shifting to manual control.
 6. A health monitoring system (HMS) for real-time prediction of pending adverse events based on continuous glucose monitoring (CGM) data, comprising the glucose rate increase detector (GRID) of claim 2 and a controller, which provides prevention of the events by either a corrective action or shifting to manual control.
 7. A health monitoring system (HMS) for real-time prediction of pending adverse events based on continuous glucose monitoring (CGM) data, comprising the glucose rate increase detector (GRID) of claim 3 and a controller, which provides prevention of the events by either a corrective action or shifting to manual control.
 8. A health monitoring system (HMS) for real-time prediction of pending adverse events based on continuous glucose monitoring (CGM) data, comprising the glucose rate increase detector (GRID) of claim 4 and a controller, which provides prevention of the events by either a corrective action or shifting to manual control.
 9. An artificial pancreas comprising the glucose rate increase detector (GRID) of claim 1, a low glucose predictor (LGP), a continuous glucose monitoring (CGM) device, a glucose controller, and an insulin pump, programmed and configured wherein the LGP receives CGM data from the CGM device and upon detection of low glucose, relays an alert to the user, and wherein the GRID receives CGM date from the CGM device and insulin delivery information, and upon detection of a meal, relays a bolus recommendation to the controller, which directs the pump to deliver the bolus.
 10. An artificial pancreas comprising the glucose rate increase detector (GRID) of claim 2, a low glucose predictor (LGP), a continuous glucose monitoring (CGM) device, a glucose controller, and an insulin pump, programmed and configured wherein the LGP receives CGM data from the CGM device and upon detection of low glucose, relays an alert to the user, and wherein the GRID receives CGM date from the CGM device and insulin delivery information, and upon detection of a meal, relays a bolus recommendation to the controller, which directs the pump to deliver the bolus.
 11. An artificial pancreas comprising the glucose rate increase detector (GRID) of claim 3, a low glucose predictor (LGP), a continuous glucose monitoring (CGM) device, a glucose controller, and an insulin pump, programmed and configured wherein the LGP receives CGM data from the CGM device and upon detection of low glucose, relays an alert to the user, and wherein the GRID receives CGM date from the CGM device and insulin delivery information, and upon detection of a meal, relays a bolus recommendation to the controller, which directs the pump to deliver the bolus.
 12. An artificial pancreas comprising the glucose rate increase detector (GRID) of claim 4, a low glucose predictor (LGP), a continuous glucose monitoring (CGM) device, a glucose controller, and an insulin pump, programmed and configured wherein the LGP receives CGM data from the CGM device and upon detection of low glucose, relays an alert to the user, and wherein the GRID receives CGM date from the CGM device and insulin delivery information, and upon detection of a meal, relays a bolus recommendation to the controller, which directs the pump to deliver the bolus.
 13. A method for providing a reliable layer of protection to insulin therapy, performed by the glucose rate increase detector (GRID) of claim 1 in conjunction with a continuous glucose monitoring (CGM) system, a controller and an insulin pump, the method comprising: operating the GRID, wherein the GRID detects in a user persistent increases in blood glucose concentration associated with meal events and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the user to bolus for a meal, during open-loop control, wherein the GRID triggers the controller to actuate the pump to deliver safe meal boluses to blunt meal peak safely, or to shift to manual control, during closed-loop control, or alerting the user to bolus for a meal, during open-loop control.
 14. A method for providing a reliable layer of protection to insulin therapy, performed by the glucose rate increase detector (GRID) of claim 2 in conjunction with a continuous glucose monitoring (CGM) system, a controller and an insulin pump, the method comprising: operating the GRID, wherein the GRID detects in a user persistent increases in blood glucose concentration associated with meal events and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the user to bolus for a meal, during open-loop control, wherein the GRID triggers the controller to actuate the pump to deliver safe meal boluses to blunt meal peak safely, or to shift to manual control, during closed-loop control, or alerting the user to bolus for a meal, during open-loop control.
 15. A method for providing a reliable layer of protection to insulin therapy, performed by the glucose rate increase detector (GRID) of claim 3 in conjunction with a continuous glucose monitoring (CGM) system, a controller and an insulin pump, the method comprising: operating the GRID, wherein the GRID detects in a user persistent increases in blood glucose concentration associated with meal events and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the user to bolus for a meal, during open-loop control, wherein the GRID triggers the controller to actuate the pump to deliver safe meal boluses to blunt meal peak safely, or to shift to manual control, during closed-loop control, or alerting the user to bolus for a meal, during open-loop control.
 16. A method for providing a reliable layer of protection to insulin therapy, performed by the glucose rate increase detector (GRID) of claim 4 in conjunction with a continuous glucose monitoring (CGM) system, a controller and an insulin pump, the method comprising: operating the GRID, wherein the GRID detects in a user persistent increases in blood glucose concentration associated with meal events and either triggers a meal bolus to blunt meal peak safely, during closed-loop control, or alerts the user to bolus for a meal, during open-loop control, wherein the GRID triggers the controller to actuate the pump to deliver safe meal boluses to blunt meal peak safely, or to shift to manual control, during closed-loop control, or alerting the user to bolus for a meal, during open-loop control.
 17. The detector of claim 1: wherein the pre-processing section filters the data using a noise-spike filter: ${G_{F,{NS}}(k)} = \left\{ \begin{matrix} {G_{m}(k)} & {{{if}\mspace{14mu}{{{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}}}} \leq {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} - {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{F,{NS}}\left( {k - 1} \right)} - {G_{m}(k)}} \right)} > {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} + {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}} \right)} > {\Delta\; G}} \end{matrix} \right.$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS) (k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement, and ΔG is the maximum allowable ROC, set to 3 mg/dL in a one-minute period, to limit the ROC to a physiologically-probable value, and the data are then passed through a low pass filter to damp high frequency fluctuations: ${{G_{F}(k)} = {{\frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}{G_{F,{NS}}(k)}} + {\left( {1 - \frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}} \right){G_{F}\left( {k - 1} \right)}}}},$ where Δt is the sampling period, τ_(F) is the filter time constant, and G_(F) is the filtered value, wherein the value for τ_(F) has been tuned to smooth the data without introducing a long delay to optimize the specificity and detection speed of the algorithm; and wherein in the estimation section, the ROC of glucose is calculated using the first derivative of a 3-point Lagrangian interpolation polynomial, evaluated at the most recent point, as, as follows: ${G_{F}^{\prime}(k)} \cong {{\frac{{t(k)} - {t\left( {k - 1} \right)}}{\left( {{t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}} \right)\left( {{t\left( {k - 2} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 2} \right)}} + {\frac{{t(k)} - {t\left( {k - 2} \right)}}{\left( {{t\left( {k - 1} \right)} - {t\left( {k - 2} \right)}} \right)\left( {{t\left( {k - 1} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 1} \right)}} + {\frac{{2\;{t(k)}} - {t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}}{\left( {{t(k)} - {t\left( {k - 1} \right)}} \right)\left( {{t(k)} - {t\left( {k - 2} \right)}} \right)}{G_{F}(k)}}}$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS)(k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement.
 18. The detector of claim 1: wherein the pre-processing section filters the data using a noise-spike filter: ${G_{F,{NS}}(k)} = \left\{ \begin{matrix} {G_{m}(k)} & {{{if}\mspace{14mu}{{{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}}}} \leq {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} - {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{F,{NS}}\left( {k - 1} \right)} - {G_{m}(k)}} \right)} > {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} + {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}} \right)} > {\Delta\; G}} \end{matrix} \right.$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS)(k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement, and ΔG is the maximum allowable ROC, set to 3 mg/dL in a one-minute period, to limit the ROC to a physiologically-probable value, and the data are then passed through a low pass filter to damp high frequency fluctuations: ${{G_{F}(k)} = {{\frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}{G_{F,{NS}}(k)}} + {\left( {1 - \frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}} \right){G_{F}\left( {k - 1} \right)}}}},$ where Δt is the sampling period, τ_(F) is the filter time constant, and G_(F) is the filtered value, wherein the value for τ_(F) has been tuned to smooth the data without introducing a long delay to optimize the specificity and detection speed of the algorithm; and wherein the detection section comprises a logic wherein the detection is positive and equal to 1 at the current point only if a corresponding filtered point is above a value (G_(min)) and (^) either the last three ROC values are above G′_(min,3) or (∨) the last two are above G′_(min,2): ${GRID}^{+} = \left\{ \begin{matrix} 1 & {{{if}\mspace{14mu}{G_{F}(k)}} > {G_{m\; i\; n}\bigwedge\left( {\left( {{G_{F}^{\prime}\left( {{k - 2}:k} \right)} > G_{{m\; i\; n},3}^{\prime}} \right)\bigvee\left( {{G_{F}^{\prime}\left( {{k - 1}:k} \right)} > G_{{m\; i\; n},2}^{\prime}} \right)} \right)}} \\ 0 & {otherwise} \end{matrix} \right.$ wherein k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, the value of G_(min) is chosen large enough to isolate post-meal glucose values and to avoid the hypoglycemia region, and the ROC cutoffs are chosen to isolate post-meal rises and the hierarchical approach, with either two at a higher ROC or three at a lower ROC, allows faster detection with higher ROC values.
 19. The detector of claim 1: wherein in the estimation section, the ROC of glucose is calculated using the first derivative of a 3-point Lagrangian interpolation polynomial, evaluated at the most recent point, as, as follows: ${G_{F}^{\prime}(k)} \cong {{\frac{{t(k)} - {t\left( {k - 1} \right)}}{\left( {{t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}} \right)\left( {{t\left( {k - 2} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 2} \right)}} + {\frac{{t(k)} - {t\left( {k - 2} \right)}}{\left( {{t\left( {k - 1} \right)} - {t\left( {k - 2} \right)}} \right)\left( {{t\left( {k - 1} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 1} \right)}} + {\frac{{2{t(k)}} - {t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}}{\left( {{t(k)} - {t\left( {k - 1} \right)}} \right)\left( {{t(k)} - {t\left( {k - 2} \right)}} \right)}{G_{F}(k)}}}$ where k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, G_(F,NS)(k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement; and wherein the detection section comprises a logic wherein the detection is positive and equal to 1 at the current point only if a corresponding filtered point is above a value (G_(min)) and (^) either the last three ROC values are above G′_(min,3) or (∨) the last two are above G′_(min,2): ${GRID}^{+} = \left\{ \begin{matrix} 1 & \begin{matrix} {{{if}\mspace{14mu}{G_{F}(k)}} > {G_{\min}\bigwedge\left( {\left( {{G_{F}^{\prime}\left( {k - {2\text{:}k}} \right)} > G_{\min,3}^{\prime}} \right)\bigvee} \right.}} \\ \left. \left( {{G_{F}^{\prime}\left( {k - {1\text{:}k}} \right)} > G_{\min,2}^{\prime}} \right) \right) \end{matrix} \\ 0 & {otherwise} \end{matrix} \right.$ wherein k is the sampling instant, G_(F,NS)(k−1) is the previous filtered value from the noise spike filter, the value of G_(min) is chosen large enough to isolate post-meal glucose values and to avoid the hypoglycemia region, and the ROC cutoffs are chosen to isolate post-meal rises and the hierarchical approach, with either two at a higher ROC or three at a lower ROC, allows faster detection with higher ROC values.
 20. The detector of claim 1: wherein the pre-processing section filters the data using a noise-spike filter: ${G_{F,{NS}}(k)} = \left\{ \begin{matrix} {G_{m}(k)} & {{{if}\mspace{14mu}{{{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}}}} \leq {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} - {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{F,{NS}}\left( {k - 1} \right)} - {G_{m}(k)}} \right)} > {\Delta\; G}} \\ {{G_{F,{NS}}\left( {k - 1} \right)} + {\Delta\; G}} & {{{if}\mspace{14mu}\left( {{G_{m}(k)} - {G_{F,{NS}}\left( {k - 1} \right)}} \right)} > {\Delta\; G}} \end{matrix} \right.$ where k is the sampling instant, G_(F,NS) (k−1) is the previous filtered value from the noise spike filter, G_(F,NS) (k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement, and ΔG is the maximum allowable ROC, set to 3 mg/dL in a one-minute period, to limit the ROC to a physiologically-probable value, and the data are then passed through a low pass filter to damp high frequency fluctuations: ${{G_{F}(k)} = {{\frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}{G_{F,{NS}}(k)}} + {\left( {1 - \frac{\Delta\; t}{\tau_{F} + {\Delta\; t}}} \right){G_{F}\left( {k - 1} \right)}}}},$ where Δt is the sampling period, τ_(F) is the filter time constant, and G_(F) is the filtered value, wherein the value for τ_(F) has been tuned to smooth the data without introducing a long delay to optimize the specificity and detection speed of the algorithm; wherein in the estimation section, the ROC of glucose is calculated using the first derivative of a 3-point Lagrangian interpolation polynomial, evaluated at the most recent point, as, as follows: ${G_{F}^{\prime}(k)} \cong {{\frac{{t(k)} - {t\left( {k - 1} \right)}}{\left( {{t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}} \right)\left( {{t\left( {k - 2} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 2} \right)}} + {\frac{{t(k)} - {t\left( {k - 2} \right)}}{\left( {{t\left( {k - 1} \right)} - {t\left( {k - 2} \right)}} \right)\left( {{t\left( {k - 1} \right)} - {t(k)}} \right)}{G_{F}\left( {k - 1} \right)}} + {\frac{{2\;{t(k)}} - {t\left( {k - 2} \right)} - {t\left( {k - 1} \right)}}{\left( {{t(k)} - {t\left( {k - 1} \right)}} \right)\left( {{t(k)} - {t\left( {k - 2} \right)}} \right)}{G_{F}(k)}}}$ where k is the sampling instant, G_(F,NS) (k−1) is the previous filtered value from the noise spike filter, G_(F,NS) (k) is the filtered value resulting from the noise-spike filter, G_(m)(k) is the measurement; and wherein the detection section comprises a logic wherein the detection is positive and equal to 1 at the current point only if a corresponding filtered point is above a value (G_(min)) and (^) either the last three ROC values are above G′_(min,3) or (∨) the last two are above G′_(min,2): ${GRID}^{+} = \left\{ \begin{matrix} 1 & \begin{matrix} {{{if}\mspace{14mu}{G_{F}(k)}} > {G_{\min}\bigwedge\left( {\left( {{G_{F}^{\prime}\left( {k - {2\text{:}k}} \right)} > G_{\min,3}^{\prime}} \right)\bigvee} \right.}} \\ \left. \left( {{G_{F}^{\prime}\left( {k - {1\text{:}k}} \right)} > G_{\min,2}^{\prime}} \right) \right) \end{matrix} \\ 0 & {otherwise} \end{matrix} \right.$ wherein k is the sampling instant, G_(F,NS) (k−1) is the previous filtered value from the noise spike filter, the value of G_(min) is chosen large enough to isolate post-meal glucose values and to avoid the hypoglycemia region, and the ROC cutoffs are chosen to isolate post-meal rises and the hierarchical approach, with either two at a higher ROC or three at a lower ROC, allows faster detection with higher ROC values. 